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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 883 records · Page 49

All sources NASA NTRS DOE OSTI

DOE OSTI2015

A COMPUTATIONAL AND EXPERIMENTAL STUDY OF METAL AND COVALENT ORGANIC FRAMEWORKS USED IN ADSORPTION COOLING

Metal-organic frameworks (MOFs) have recently attracted enormous interest over the past few years due to their potential applications in energy storage and gas separation. However, there have been few reports on MOFs for adsorption cooling applications. Adsorption cooling technology is an established alternative to mechanical vapor compression refrigeration systems. Adsorption cooling is an excellent alternative in industrial environments where waste heat is available. Applications also include hybrid systems, refrigeration, power-plant dry cooling, cryogenics, vehicular systems and building HVAC. Adsorption based cooling and refrigeration systems have several advantages including few moving parts and negligible power consumption. Key disadvantages include large thermal mass, bulkiness, complex controls, and low COP (0.2-0.5). We explored the use of metal organic frameworks that have very high mass loading and relatively low heats of adsorption, with certain combinations of refrigerants to demonstrate a new type of highly efficient adsorption chiller. An adsorption chiller based on MOFs suggests that a thermally-driven COP>1 may be possible with these materials, which would represent a fundamental breakthrough in performance of adsorption chiller technology. Computational fluid dynamics combined with a system level lumped-parameter model have been used to project size and performance for chillers with a cooling capacity ranging from a few kW to several thousand kW. In addition, a cost model has been developed to project manufactured cost of entire systems. These systems rely on stacked micro/mini-scale architectures to enhance heat and mass transfer. Presented herein are computational and experimental results for hydrophyilic MOFs, fluorophilic MOFs and also flourophilic Covalent-organic frameworks (COFs).

adsorption↗

DOE OSTI2014

Provenance In Sensor Data Management: A Cohesive, Independent Solution

In today's information-driven workplaces, data is constantly undergoing transformations and being moved around. The typical business-as-usual approach is to use email attachments, shared network locations, databases, and now, the cloud. More often than not, there are multiple versions of the data sitting in different locations and users of this data are confounded by the lack of metadata describing its provenance, or in other words, its lineage. Our project is aimed to solve this issue in the context of sensor data. The Oak Ridge National Laboratory's Building Technologies Research and Integration Center has reconfigurable commercial buildings deployed on the Flexible Research Platforms (FRPs). These FRPs are instrumented with a large number of sensors which measure a number of variables such as HVAC efficiency, relative humidity, and temperature gradients across doors, windows, and walls. Sub-minute resolution data from hundreds of channels is acquired. This sensor data, traditionally, was saved to a shared network location which was accessible to a number of scientists for performing complicated simulation and analysis tasks. The sensor data also participates in elaborate quality assurance exercises as a result of inherent faults. Sometimes, faults are induced to observe building behavior. It became apparent that proper scientific controls required not just managing the data acquisition and delivery, but to also manage the metadata associated with temporal subsets of the sensor data. We built a system named ProvDMS, or Provenance Data Management System for the FRPs, which would both allow researchers to retrieve data of interest as well as trace data lineage. This provides researchers a one-stop shop for comprehensive views of various data transformation allowing researchers to effectively trace their data to its source so that experiments, and derivations of experiments, may be reused and reproduced without much overhead of the repeatability of experiments that use it. Using these traces, researchers can determine exactly what happens to data as it moves through its life cycle.

Hensley, Zachary P↗

DOE OSTI2017

Demand Response and Smart Buildings: A Survey of Control, Communication, and Cyber-Physical Security

In this paper, we perform a comprehensive survey of the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs); the power architecture with multibus characteristics; different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings; the communication technologies and network architectures; and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. Finally, the current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.

42 ENGINEERING↗

DOE OSTI2017

Long term occupancy estimation in a commercial space: an empirical study: poster abstract

Understanding occupancy patterns in a building is very useful to control HVAC systems for improving energy efficiency of the building and occupant comfort. There has been a very little attempt to understand long term occupancy patterns in a commercial space. In this work, we leverage depth sensors (Kinect for XBOX One) to collect occupancy count from an 11,000 square foot commercial space (Bosch office) for nine months. We analyze the collected data and describe key findings that provide deep insights about how a commercial space is used and serve as a guideline to formulate novel and efficient control strategies.

Liu, Kin Sum↗

DOE OSTI2023

Data-Driven Co-optimization of Energy Efficiency and Indoor Environmental Quality in Commercial Buildings

In this paper, we use publicly available data of a highly instrumented building to estimate how zonal temperature and carbon dioxide (CO2) concentration are related to some key operational and environmental measurements. Subsequently, we have developed, simulated, and evaluated an optimization framework for minimizing the energy consumption of the central heating, ventilation and air conditioning (HVAC) unit while meeting zonal temperature and indoor air quality (IAQ) standards. Finally, we have evaluated the achievable energy savings for our proposed approach as compared to a baseline approach and reported significant savings potential.

Naqvi, Syed Ahsan Raza↗

DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗

DOE OSTI2015

VOLTTRON Version 3.x

VOLTTRON™ is an agent execution platform providing services to its agents that allow them to easily communicate with physical devices and other resources. VOLTTRON™ delivers an innovative distributed control and sensing software platform that supports modern control strategies, including agent-based and transaction-based controls. It enables mobile and stationary software agents to perform information gathering, processing, and control actions. VOLTTRON™ can independently manage a wide range of applications, such as HVAC systems, electric vehicles, distributed energy or entire building loads, leading to improved operational efficiency.

Source record↗

DOE OSTI2016

VOLTTRON Version 4

Source record↗

DOE OSTI2017

VOLTTRON Version 5.0

VOLTTRON is an agent execution platform providing services to its agents that allow them to easily communicate with physical devices and other resources. VOLTTRON delivers an innovative distributed control and sensing software platform that supports modern control strategies, including agent-based and transaction-based controls. It enables mobile and stationary software agents to perform information gathering, processing, and control actions. VOLTTRON can independently manage a wide range of applications, such as HVAC systems, electric vehicles, distributed energy or entire building loads, leading to improved operational efficiency.

Source record↗

DOE OSTI2018

funnel v0.0.1

fcompare is a software package that applies for comparing two curves generated from datasets in csv format. A funnel is generated around the reference curve and the other curve is checked to verify if it is inside the funnel. The funnel can be reshaped by setting a tolerance. One potential application is to verify the development of building HVAC control sequence, in which it compares data curves from real operation with data curves from simulation using developed control sequences, to verify if the developed control sequences have been implemented correctly. In addition, it can be used for Modelica library developer to do the unit test that verifies new model developments.

Wetter, Michael↗

DOE OSTI2019

Commercial Building Energy Saver, API (CBES API) v2.0

The CBES API provides Application Programming Interface to a suite of functions to improve energy efficiency of buildings, including building energy benchmarking, preliminary retrofit analysis using a pre-simulation database DEEP, and detailed retrofit analysis using energy modeling with the EnergyPlus simulation engine. New capabilities in version 2.0 include renewable technologies (PV, storage), advanced HVAC systems, demand response measures, TDV (Time Dependent Valuation energy) metrics, as well as shading from adjacent buildings, trees or roof obstructions. The CBES API is used to power the LBNL CBES Web App. It can be used by third party developers and vendors.

Hong, Tianzhen↗

DOE OSTI2019

Modelica Buildings Library

The Building Informatics Environment is a modeling environment based on the Modelica language. The environment allows users to create a computer model of a building and its energy systems with various time scales and physical resolutions. The environment can be used for rapid development of e.g. demand controls algorithms new HVAC system solutions and new operational strategies. Models for building energy and control systems are made available in the environment. The models can be used as provided or they can be changed or linked with each other in order to model the effects that a particular user is interested in.

Wetter, Michael↗

DOE OSTI2019

AirflowNetwork [SWR 19-45]

The AirflowNetwork software provides the ability to simulate air movement and the resulting transport of contaminants and other materials within a building. The performance of air distribution systems, including supply and return leaks, and multizone airflows driven by outdoor wind and forced air during HVAC system operation are simulated using a nodal network representation of the building. The pressure network model implemented here is based on George Walton's AIRNET and was modified for inclusion in the EnergyPlus building energy modeling software. The algorithm were then extracted from EnergyPlus into a stand-alone package to facility use without the building energy model and more extensive testing and validation of the underlying operations.

DeGraw, Jason↗

DOE OSTI2019

guideline36_conformance_test 0.1

The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) has established a set of standardized high performance sequences of operation for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings through their "Guideline 36". In order to achieve the intended performance, these sequences must be translated and programmed accurately in the Building Automation System (BAS) controllers. Standardizing these sequences will allow for a more efficient product delivery mechanism where the sequences are programmed and tested centrally by each manufacturer, and then distributed to their dealers/engineering contractors. This approach would minimize the need for each installer to re-interpret and program the sequences, reduces risk of errors, and reduces the time required for commissioning in the field. To achieve this goal, a performance validation method is needed to provide independent confirmation that each manufacturer has programmed the Guideline 36 sequences accurately. A standardized method of test would also avoid the need for manual interpretation (and the associated human variability) that is required with typical functional testing approaches by automating the inputs and the range of expected responses. This software "guideline36_conformance_test" has been developed to conduct standardized, repeatable and manufacturer independent tests to validate that a BAS controller has been programmed in conformance with Guideline 36. Manufacturers would provide the controller (or the control program) and the software would run a suite of tests by setting a set of inputs to the controller and verifying the output signals from the controller matches the expected output as set by Guideline 36.

Pritoni, Marco↗

DOE OSTI2015

HEMS: Home Energy Management System [SWR-15-12] - Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources

The HEMS algorithm schedules residential appliance operation to minimize the weighted sum of occupant discomfort, total energy cost, peak electricity consumption and carbon footprint, weighted according to occupant preferences. A centralized mixed integer linear programming (MILP) based optimization approach is taken, with the HEMS managing all appliances directly, following a Model Predictive Control (MPC) methodology which determines the optimal schedule over the user-specified scheduling horizon and repeats the optimization at configurable time intervals. The HEMS is developed on the General Algebraic Modeling System (GAMS) using the MILP solver. This system is able to effectively deal with the stochasticity in the distributed system by utilizing Monte Carlo simulation where uncertainties in future electricity price, outdoor temperature, water usage, solar and wind generation can be accounted for. The HEMS controls the operation of the heating, ventilation and air conditioning (HVAC) system, water heater, refrigerator, residential batteries, electric vehicles, standalone micro combined heat and power (CHP) generator, dishwasher, washer/dryer, pool pumps, and lighting.

Pratt, Annabelle↗

DOE OSTI2020

MBMS1.0: An Open-Source Code for Modeling and Simulation of Membrane-Based Dehumidification and Energy Recovery

Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost and very limited energy consumption. Developing a simple and efficient open-source code simulation tool is important for boosting the optimization and evaluation of such device in HVAC community. This paper reports a first-order physics based model which accounts for the fundamental heat and mass transfer of humid-air vapor at feed side to flow stream at permeate side. The current model comprises two membrane mass transfer submodels (i.e. microstructure model and performance map model); and it adopts a segment-by-segment methodology for discretizing heat and mass transfer governing equations. The model is capable of simulating both dehumidifiers and energy recovery ventilators with parallel-flow cross-flow, and counter-flow configurations. The model was validated with the measurements at appropriate device. The practices in dehumidification and energy recovery exchangers are also discussed. The model and open-source codes are expected to become a solid fundament for developing a more comprehensive and accurate membrane-based dehumidification in the future.

Gao, Zhiming↗

DOE OSTI2021

FlexDRL v1.0

FlexDRL is a framework that can be used as a benchmarking tool to evaluate different Deep Reinforcement Learning algorithms in a building energy management setting. OpenAI Gym was used as wrapper to the co-simulation environment (EnergyPlus for envelope and HVAC and Modelica for battery and PV) to interface with DRL algorithms, which are developed in PyTorch. In order to facilitate the usage of FlexDRL, all the required dependencies have been packaged into a Docker container.

Touzani, Samir↗

DOE OSTI2020

OpenStudio System-level KPI Reporting Measure v1.0

This is a OpenStudio measure that calculates and reports the system-level key performance indicators (KPIs) for evaluating building energy performance at the system level. The systems are defined by the service it provides in commercial buildings, which include (1) lighting, (2) Miscellaneous Electric Loads (MELs), (3) Heating, Ventilation and Air Conditioning (HVAC), and (4) Service Water Heating (SWH).

Li, Han↗